Cross-validation in SVM

Cross-validation in SVM

Dear David, Dear R Users,

Calculation of Cross-Validation for SVM, with thoese time series which include negative and positive values ( for example return of a stock exchange index) must be different from a calculation of Cross-Validation with time series which includes just absolute values( for example a stock exchange index).
How is it calculated for a return time series?
Thank you very much for any help.
Amir

Re: Cross-validation in SVM

On Thu, 23 Feb 2006, Amir Safari wrote:

> Calculation of Cross-Validation for SVM, with thoese time series which
> include negative and positive values ( for example return of a stock
> exchange index) must be different from a calculation of Cross-Validation
> with time series which includes just absolute values( for example a
> stock exchange index).

Not necessarily, depends on the type of data.

> How is it calculated for a return time series?

>From the man page of svm():

cross: if a integer value k>0 is specified, a k-fold cross
validation on the training data is performed to assess the
quality of the model: the accuracy rate for classification
and the Mean Squared Error for regression

binomial models with too many 1s???

Dear R users,

Does anyone know a solution for the problem when there are
too many ones or zeros in the respons of a binomial model?
I think this means that the data are over/under despersed
and the result is very bad model fit.

I'm using glmmPQL(family=quasibinomial) to fit a model to
my data, but the model estimates are not in the range they
should be due to overdispersion (or under?) What shall I
do? Is there a model type for this kind of data? I would
prefer to keep all may data, otherwise I could also select
some of the ones so that their number will be equal to the
number of zeros. But I don't thik this is the right way...

Re: binomial models with too many 1s???

I take it that a zero inflated negative binomial (i.e. Poisson) regression
model is what you are trying to fit, aka ZIP? If so try looking at the
documentation for the zicounts package for R, for one. Of course, you can
also search on these keywords yourself, to find exactly what you want....

Does anyone know a solution for the problem when there are too many ones or
zeros in the respons of a binomial model?
I think this means that the data are over/under despersed and the result is
very bad model fit.

I'm using glmmPQL(family=quasibinomial) to fit a model to my data, but the
model estimates are not in the range they should be due to overdispersion
(or under?) What shall I do? Is there a model type for this kind of data? I
would prefer to keep all may data, otherwise I could also select some of the
ones so that their number will be equal to the number of zeros. But I don't
thik this is the right way...